Generally, relational data in graph form can be used to represent networks in many different types of domains, such as Internet-related networks, in the fields of science and research collaboration, in epidemiology related to the study of patterns and causes of health conditions, communication analysis between members and groups in social networks, advertising and marketing analytics, in the study of ecosystems, power grid dynamics, and many others. Many of these types of networks are dynamic and the network entities that are represented as nodes in a graph continuously evolve over time. Generally, the links or connections between the network entities may be represented as any form of citations, collaborations, associations, functions, communications, co-locations, shared mechanisms, or many other explicit or implicit relationships. These network entity connections also change and evolve over time with the addition, deletion, and updates of various network entities in a naturally dynamic network.
The temporal information related to the changes over time in a dynamic network is an often overlooked, but important factor needed to accurately model, predict, and understand network data. Despite the importance and ubiquity of these dynamics, such as for user events, user actions, and interactions in many real-world networks, conventional approaches to predicting, analyzing, and modeling dynamic networks do not take into account the temporal information in network data. Rather, typical analysis techniques propose to model a dynamic network as a sequence of static snapshot graphs, where each static snapshot graph represents the edges (e.g., the network entity connections) that occur over a user-specified discrete-time interval, such as for one day or during a week. This is a very coarse approximation of an actual dynamic network that is continuously evolving and changing over time, also commonly referred to as a continuous-time dynamic network (CTDN). In addition to the loss of temporal information using static snapshots of graph representations, many other issues, such as selecting an appropriate aggregation granularity which is a challenging problem in itself, can lead to poor predictive performance or misleading results when attempting to model, predict, and understand the network data for a dynamic network.